DA0-002 Visualization and Reporting Practice Question
You are a data analyst at a logistics company. You have created a dashboard to monitor delivery performance. The dashboard includes a scatter plot showing delivery time (hours) vs. distance (miles) for each delivery, with points colored by delivery region (A, B, C, D, E). Users have reported that the scatter plot is cluttered because there are over 10,000 points, making it hard to see patterns. Additionally, the color legend for the five regions uses similar shades of blue, making it difficult to distinguish which region a point belongs to. You need to improve the scatter plot to reduce overplotting and improve region differentiation. Which approach is most effective?
⚠ Common exam trap
Candidates often choose small multiples (Option B) thinking they reduce clutter, but the question specifically asks to improve differentiation and reduce overplotting in a single view, and small multiples fragment the data, making cross-region comparison harder.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set point opacity to 30% and use a qualitative color palette with distinct hues for each region
Reducing opacity (alpha blending) mitigates overplotting by making overlapping points more transparent, while switching to a qualitative color palette (e.g., distinct hues like red, green, blue) ensures each of the five regions is easily distinguishable. This directly addresses both user complaints without losing the overall distribution context.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a hexagonal binning plot (hexbin) with color representing region
Why it's wrong here
Hexagonal binning aggregates points into bins, so colouring by region would require separate hexbin layers whose overlapping bins obscure one another, defeating region differentiation. Hexbin suits dense single-series data where aggregate density matters, not five categorical groups needing distinct colours.
- ✗
Create five separate scatter plots (small multiples) for each region
Why it's wrong here
Small multiples split the data into five charts, so cross-region comparison and overall distance-versus-time patterns are lost, and each panel still overplots its points. It is tempting because separating series aids differentiation, but the stem needs reduced overplotting and clear region distinction within one view, which transparency and a colourblind-safe palette address.
- ✓
Set point opacity to 30% and use a qualitative color palette with distinct hues for each region
Why this is correct
Reducing point opacity to 30% lets overlapping marks accumulate into darker density, revealing clusters across 10,000 points. A qualitative palette assigns each of the five regions a perceptually distinct hue, replacing the near-identical blues that currently prevent region differentiation.
- ✗
Convert to a bubble chart by adding package weight as bubble size
Why it's wrong here
Adding package weight as bubble size leaves the 10,000-point overplotting untouched and keeps the five near-identical blue shades, so neither reported problem is addressed. Bubble charts suit encoding a third measure across a modest point count; here the stem demands overplotting reduction and distinguishable region colours.
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